Forecasting MA model different to manually computation?

Thanks, but this does not help me, because first of all, I do not know
how to look at the source code (just entering fitted() or
getAnywhere(fitted()) does not help,

second, your solution x-m$residuals does not be a solution, because
then the question is, where do the residuals come from?

2013/5/22 Rui Barradas <ruipbarradas at sapo.pt>:

Hello,

Since R is open source, you can look at the source code of package forecast
to know exactly how it is done. My guess would be

x - m$residuals

Time Series:
Start = 1
End = 3
Frequency = 1
[1] 3.060660 4.387627 3.000000


Hope this helps,

Rui Barradas

Em 22-05-2013 15:13, Neuman Co escreveu:

Hi,
3 down vote favorite
1

I am interested in forecasting a MA model.Therefore I have created a
very simple data set (three variables). I then adapted a MA(1) model
to it. The results are:

x<-c(2,5,3)
m<-arima(x,order=c(0,0,1))

Series: x
ARIMA(0,0,1) with non-zero mean

Coefficients:
            ma1  intercept
        -1.0000     3.5000
s.e.   0.8165     0.3163

sigma^2 estimated as 0.5:  log likelihood=-3.91
AIC=13.82   AICc=-10.18   BIC=11.12

While the MA(1) model looks like this:

X_t=c+a_t+theta*a_{t-1}

and a_t is White Noise.

Now, I look at the fitted values:

library(forecast)
fitted(m)
Time Series:
Start = 1
End = 3
Frequency = 1
[1] 3.060660 4.387627 3.000000

I tried different ways, but I cant find out how the fitted values
(3.060660, 4.387627 and 3.000000) are calculated.

Any help would be very appreciated.



--
Neumann, Conrad